25 citations · 52 across the 7 of their papers we have counts for
9 papers
Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples
Henrik Imberg, Xiaomi Yang, Carol Flannagan +1
Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adapti…
Robust Model-based Inference for Non-Probability Samples
Ali Rafei, Michael R. Elliott, Carol A. C. Flannagan
With the ubiquitous availability of unstructured data, growing attention is paid as how to adjust for selection bias in such non-probability samples. The majority of the robust est…
Robust and Efficient Bayesian Inference for Non-Probability Samples
Ali Rafei, Michael R. Elliott, Carol A. C. Flannagan
The declining response rates in probability surveys along with the widespread availability of unstructured data has led to growing research into non-probability samples. Existing r…
Robust Bayesian Inference for Big Data: Combining Sensor-based Records with Traditional Survey Data
Ali Rafei, Carol A. C. Flannagan, Brady T. West +1
Big Data often presents as massive non-probability samples. Not only is the selection mechanism often unknown, but larger data volume amplifies the relative contribution of selecti…
Driver Behavior Extraction from Videos in Naturalistic Driving Datasets with 3D ConvNets
Hanwen Miao, Shengan Zhang, Carol Flannagan
Naturalistic driving data (NDD) is an important source of information to understand crash causation and human factors and to further develop crash avoidance countermeasures. Videos…
Multitasking additional-to-driving: Prevalence, structure, and associated risk in SHRP2 naturalistic driving data
András Bálint, Carol A. C. Flannagan, Andrew Leslie +3
This paper 1) analyzes the extent to which drivers engage in multitasking additional-to-driving (MAD) under various conditions, 2) specifies odds ratios (ORs) of crashing associate…